Multimodal Vision-Language Models for Automated Property Feature Extraction: A Comparative Analysis of Image, Text, and Combined Inputs - Campos, Pedro M. L.; Ribeiro, Gustavo R.; Marques, Enzo L.; Pereira, Gustavo L. B.; Pascoal, Luiz M. L.; Federson, Fernando M.; Oliveira, Sávio S. T. de (Anais da Escola Regional de Informática de Goiás (ERI-GO); 2025: Anais da XIII Escola Regional de Informática de GoiásDO - 10.5753/erigo.2025.17120, 2025)
Manual property assessment for taxation is costly, time-consuming, and subjective. This paper investigates Vision-Language Models (VLMs) to automate this task through comprehensive evaluation across image-only, text-only, and combined inputs. We evaluated six models from the Gemini and Gemma families on 200 properties from Goiânia, Brazil, classified across 11 legally-defined construction categories using specialized prompting strategies. Our analysis reveals a counterintuitive finding: text-only inputs achieve the highest accuracy, outperforming image-only and matching combined multimodal approaches. This demonstrates that structured textual descriptions contain exceptionally high signal value for legally-defined tasks.